By a Senior Engineer | December 11, 2025
I sat across the table—well, the Zoom window—from a bright, energetic twenty-two-year-old candidate last Tuesday. Let’s call him Alex.
Alex’s portfolio was spectacular. In the last six months, he had shipped three full-stack SaaS applications, a React Native mobile app, and a custom RAG (Retrieval Augmented Generation) pipeline for a local charity. On paper, he was more productive than some senior engineers I worked with ten years ago. His GitHub history was a solid wall of green squares.
I asked him to walk me through the architecture of his RAG app. He was eloquent. He talked about vector embeddings, Pinecone, Next.js server actions, and the latency trade-offs of different LLM models. He sounded like a Principal Architect.
Then, I asked him a simple, boring question.
“I see you’re using a useEffect hook here to fetch the data. What happens if the component unmounts before the request finishes? How are you handling the cleanup to prevent a memory leak?”
Alex froze. He stared at the code as if he was seeing it for the first time. He hesitated. “Uh… I think Next.js handles that?”
He didn’t know. He didn’t know because he didn’t write that code. Cursor wrote it. Claude wrote it. Copilot wrote it. Alex had acted as the product manager, telling the AI what he wanted, but he had never actually wrestled with the asynchronous nature of JavaScript. He had skipped the “Junior Developer” phase entirely and jumped straight to “Software Architect.”
And that terrifies me.
We are witnessing the extinction of the Junior Developer. In their place, we are raising a generation of “AI Conductors”—people who can orchestrate massive symphonies of code but cannot play a single instrument.
The Death of the “Struggle”
When I learned to code fifteen years ago, it was a miserable, humiliating process.
I remember spending an entire weekend trying to center a div. I remember crying over a segmentation fault in C++ that turned out to be a missing semicolon. I remember the physical sensation of my brain overheating as I tried to mentally trace the execution flow of a recursive function.
We call this “The Struggle.”
At the time, I hated it. I thought it was a barrier to entry. But looking back, I realize that the struggle was the education.
That weekend I spent fighting CSS didn’t just teach me how to center a div; it taught me how the browser rendering engine interprets the box model. That segmentation fault didn’t just teach me syntax; it taught me how memory management actually works under the hood.
The struggle builds “mental models.” It creates a deep, intuitive map of how computers work. When you spend ten hours debugging a race condition, you never forget what a race condition looks like. You develop a “spidey sense” for dangerous code.
Today, AI tools like Cursor and Windsurf have removed the struggle.
If you are a new developer in 2025, you don’t fight the CSS. You type Cmd+K and say, “Center this div and make it look modern.” The AI spits out twenty lines of perfect Tailwind classes. You hit “Accept.” It works. You move on.
You have solved the problem, but you have learned nothing. You have lifted the weight with a forklift instead of your arms. The job got done, but you didn’t build any muscle.
The Illusion of Competence
The danger here is that these new developers feel competent. They feel powerful. And to the outside observer—the Project Manager or the CEO—they look incredibly productive.
I recently reviewed a Pull Request from a “Junior” on my team who uses AI heavily. The feature was complex: a real-time notification system using WebSockets.
The code looked beautiful. It was well-commented. It used modern patterns. It had error handling. It looked like it was written by a Senior Engineer.
But when I pulled it down and ran it under load, the server crashed immediately.
Why? Because the AI had implemented a “retry logic” that created an infinite loop if the socket disconnected, effectively DDoS-ing our own server.
The Junior Developer didn’t catch it because he didn’t understand the underlying logic. He just saw that the AI produced code that passed the unit tests. He trusted the “Black Box.”
This is the “IKEA Effect” of Coding. You can assemble a beautiful piece of furniture by following the instructions (or letting the AI do it), but if a leg breaks, you have no idea how to fix it because you don’t know how joinery works. You aren’t a carpenter; you’re an assembler.
The “Black Box” Problem
We are creating a massive dependency risk.
I use these tools too. I love them. I use Claude Opus to write boilerplate, to generate regex, and to refactor messy functions. But I am a veteran. I know when the AI is lying to me. I know when the code “smells” wrong.
The new generation doesn’t have that nose. They are flying a plane on autopilot without knowing how to land manually.
This creates a terrifying fragility in our software. As codebases grow larger and more complex—generated at lightning speed by AI—we are accumulating “AI Debt.” We are building massive skyscrapers on foundations that nobody fully understands.
When something breaks—and software always breaks—who is going to fix it?
The AI? Maybe. But AI is terrible at debugging novel, systemic issues. It is great at “Make this blue,” but terrible at “Why is the database latency spiking only on Tuesdays?” That requires a mental model of the entire system, a deep understanding of the network stack, the database internals, and the application logic.
If we haven’t trained developers who understand those layers—because they skipped straight to the high-level architecture—we are going to have systems that are unmaintainable.
The Review: A Week with a “No-Code” Coder
To test this theory, I spent a week pair-programming with a brilliant bootcamp grad who swears by the “AI-First” workflow.
We were building a simple dashboard. The speed was intoxicating.
“Make a chart showing user growth,” he prompted. Boom. Chart.
“Add a dark mode toggle,” he prompted. Boom. Toggle.
“Connect this to the Supabase backend,” he prompted. Boom. Connected.
We built an MVP in four hours that would have taken me three days in 2015.
But then, we hit a wall.
We needed to customize the chart library to handle a very specific edge case involving time zones. The AI kept hallucinating methods that didn’t exist in the library’s API. It would generate code, the app would crash, and the AI would say, “Apologies, let me try again,” and generate different wrong code.
My pair partner was helpless. He tried to re-prompt the AI ten different ways. “Fix the chart.” “Use the correct timezone method.” “Read the docs.”
He didn’t know how to go into the node_modules folder, read the source code of the library, and figure out what method we actually needed. He didn’t know how to console log the object to see its prototype.
He was stuck in a loop of “Prompt and Pray.”
I eventually took the keyboard. I opened the documentation (which the AI had hallucinated). I found the correct prop. I fixed it in two lines of code.
The look on his face wasn’t relief; it was confusion. “How did you know to look there?” he asked.
“Because I’ve fought with date libraries for ten years,” I said. “And I know they all suck in the same way.”
You can’t prompt that kind of intuition. You have to earn it.
The Future of Mentorship
So, is the sky falling? Are we doomed?
Not necessarily. But we need to radically change how we hire and train Junior Developers.
We need to stop being impressed by the volume of code they produce. In an AI world, producing code is free. It is no longer a signal of skill.
Instead, we need to interview for Curiosity and Debugging.
- Don’t ask them to write a sorting algorithm on a whiteboard. (The AI can do that).
- Break the code intentionally and ask them to fix it without AI.
- Ask them to explain how a specific library works under the hood.
We also need to bring back “Hard Mode.”
If you are a Junior Developer reading this, I have a challenge for you: Turn off the AI.
Pick one day a week—maybe “No-Copilot Fridays.” Force yourself to write the code manually. Force yourself to read the documentation. Force yourself to get stuck.
Embrace the frustration. That feeling of hitting a wall? That is the feeling of learning. That is your brain building new neural pathways.
If you let the AI solve every puzzle for you, you are cheating yourself out of the only thing that matters in this career: Mastery.
We don’t need more “Architects” who can draw boxes on a whiteboard. We have plenty of those. We need builders. We need plumbers. We need mechanics. We need people who aren’t afraid to get their hands dirty in the grease and the gears of the machine.
Because when the lights go out and the autopilot fails, the person who knows how to fly the plane is the only one who matters.